
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
from pandas_profiling import ProfileReport

from sklearn.preprocessing import StandardScaler

import warnings
warnings.simplefilter(action='ignore', category=UserWarning)
warnings.simplefilter(action='ignore', category=FutureWarning)


df= pd.read_csv('C:\\Users\\dauam4\\Downloads\\winequality-red.csv')


ProfileReport(df)


sns.countplot(df['quality'])

<AxesSubplot:xlabel='quality', ylabel='count'>


list=[3, 4, 5, 6, 7, 8]
for i in list:
    print(i, df[df['quality'] == i].duplicated().sum())

3 0
4 0
5 104
6 103
7 32
8 1


df_not_duplicate=df.copy()
df_not_duplicate.drop_duplicates(keep ='first', inplace=True)
df_not_duplicate.reset_index(inplace=True)


df_not_duplicate['quality'].value_counts()

5    577
6    535
7    167
4     53
8     17
3     10
Name: quality, dtype: int64


df1=df_not_duplicate.copy()
df2=df_not_duplicate.copy()


grouped_data=df1.groupby('quality').median()


grouped_data.drop('index', axis = 1, inplace=True)


lable=[3,4,5,6,7,8]
for i in range(len(grouped_data.columns)):
    sns.barplot(lable,grouped_data.iloc[:,i])
    plt.show()


from sklearn.preprocessing import MinMaxScaler
df3=df_not_duplicate.copy()
min_max_scaler = MinMaxScaler()
normal_data=min_max_scaler.fit_transform(df3)


normal_data=pd.DataFrame(normal_data, columns=df3.columns)


normal_data['quality']=df3['quality']


normal_data.drop('index', axis=1, inplace=True)


normal_data


norm_grouped=normal_data.groupby('quality').median()


norm_grouped


lable=[3,4,5,6,7,8]
for i in range(len(norm_grouped.columns)):
    sns.barplot(lable,norm_grouped.iloc[:,i])
    plt.show()


lable=[3,4,5,6,7,8]
for i in normal_data.columns:
    sns.distplot(normal_data[i], color= 'g')
    plt.show()


plt.boxplot(normal_data.drop('quality', axis=1))
plt.show()


for i in df1.columns:
    print(i)
    sns.boxplot(df1[i])
    plt.show()

index

fixed acidity

volatile acidity

citric acid

residual sugar

chlorides

free sulfur dioxide

total sulfur dioxide

density

pH


    
    def treat_outliers(Data, i):
        Q1=Data.iloc[:,i].quantile(0.25)
        Q3=Data.iloc[:,i].quantile(0.75)
        IQR= Q3-Q1
        upper_limit = Q3 + 1.5*IQR
        lower_limit =  Q1 - 1.5*IQR

        for j in range(len(Data)):
            if ((Data.iloc[j,i] >= lower_limit) & (Data.iloc[j,i] <= upper_limit)):
                Data.iloc[j,i] = Data.iloc[j,i]
            else:
                Data.iloc[j,i] = np.nan
        
        return Data


df_treated = df1.copy()
for x in range(len(df_treated.columns)-1):
#    print(x)
    df_treated=treat_outliers(df_treated,x)


df_treated2=df_treated.copy()
df_treated2.drop('index', axis=1, inplace=True)


df_treated2


df_treated2.fillna(1000, inplace=True)
for i in range(len(df_treated2.columns)):
    for j in range(len(df_treated2)):
        if df_treated2.iloc[j,i]==1000:
            if df_treated2.iloc[j,11]==3:
                df_treated2.iloc[j,i]=grouped_data.iloc[0,i]
            elif df_treated2.iloc[j,11]==4:
                df_treated2.iloc[j,i]=grouped_data.iloc[1,i]
            elif df_treated2.iloc[j,11]==5:
                df_treated2.iloc[j,i]=grouped_data.iloc[2,i]
            elif df_treated2.iloc[j,11]==6:
                df_treated2.iloc[j,i]=grouped_data.iloc[3,i]
            elif df_treated2.iloc[j,11]==7:
                df_treated2.iloc[j,i]=grouped_data.iloc[4,i]
            else:
                df_treated2.iloc[j,i]=grouped_data.iloc[5,i]


df_treated2.drop_duplicates(keep ='first', inplace=True)


df_treated2.reset_index(inplace=True)


df_treated2.describe()


df_std=df_treated2.copy()
df_std.drop('quality', axis=1, inplace=True)
scaler=StandardScaler()
scaler.fit(df_std)

x_scaled=scaler.transform(df_std)
y_not_scaled=df_treated2['quality']


df_scaled=pd.DataFrame(x_scaled)
df_scaled['y']=y_not_scaled


df_scaled.columns=df1.columns


df_scaled.drop('index', axis =1, inplace=True)


df_scaled


sns.pairplot(df_scaled, hue='quality')

<seaborn.axisgrid.PairGrid at 0x1c275882730>


scaled_grouped_data=df_scaled.groupby('quality').median()


lable=['3', '4', '5','6','7',8]
for i in range(len(scaled_grouped_data.columns)):
    sns.barplot(lable,scaled_grouped_data.iloc[:,i])
    plt.show()


fig, ax = plt.subplots(figsize=(10,10))
sns.heatmap(df_scaled.corr(), annot =True)

<AxesSubplot:>


bins = (2,4.5,6.5, 9)
group_names = ['0', '1', '2']
df_scaled['quality'] = pd.cut(df_scaled['quality'], bins = bins, labels = group_names)


df_scaled


x_feature=df_scaled.drop(['quality'], axis=1)
y_target=df_scaled['quality']


x_feature.shape, y_target.shape

((1356, 11), (1356,))


from imblearn.combine import SMOTETomek
from imblearn.under_sampling import TomekLinks


SMOTETomek(tomek=TomekLinks(sampling_strategy='majority'))

SMOTETomek(tomek=TomekLinks(sampling_strategy='majority'))


x,y= SMOTETomek(tomek=TomekLinks(sampling_strategy='majority'),random_state=354).fit_resample(x_feature, y_target)


x.shape, y.shape

((3333, 11), (3333,))


y.value_counts()

0    1111
1    1111
2    1111
Name: quality, dtype: int64


sns.countplot(y)

<AxesSubplot:xlabel='quality', ylabel='count'>


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=100, stratify=y)


from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_validate
from sklearn.model_selection import StratifiedKFold
skfold=StratifiedKFold(n_splits=10, shuffle=True, random_state=354)


from sklearn.tree import DecisionTreeClassifier
clf=cross_val_score(DecisionTreeClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.82042254 0.81338028 0.82394366 0.82685512 0.86925795 0.8409894
 0.85159011 0.81272085 0.87279152 0.83038869]
0.8362340118449211


from sklearn.svm import SVC
clf=cross_val_score(SVC(kernel = 'rbf'),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.86971831 0.86267606 0.85211268 0.83745583 0.8869258  0.86925795
 0.87632509 0.85159011 0.89399293 0.84805654]
0.8648111282536206


from sklearn.ensemble import RandomForestClassifier
clf=cross_val_score(RandomForestClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.94014085 0.91901408 0.91901408 0.90106007 0.9434629  0.94699647
 0.94699647 0.90459364 0.9434629  0.9434629 ]
0.9308204349773552


from sklearn.neighbors import KNeighborsClassifier
clf=cross_val_score(KNeighborsClassifier(n_neighbors= 5),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.84859155 0.8415493  0.8943662  0.85159011 0.85512367 0.86925795
 0.86572438 0.83392226 0.8869258  0.83392226]
0.8580973473348926


from sklearn.naive_bayes import GaussianNB
clf=cross_val_score(GaussianNB(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.67253521 0.63732394 0.69014085 0.63957597 0.71378092 0.63957597
 0.68551237 0.63250883 0.65371025 0.69611307]
0.6660777385159011


from xgboost import XGBClassifier
clf=cross_val_score(XGBClassifier(eval_metric='mlogloss'),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.93309859 0.91549296 0.92605634 0.9434629  0.96113074 0.95759717
 0.93639576 0.8975265  0.96113074 0.9540636 ]
0.9385955307818643


from catboost import CatBoostClassifier
clf=cross_val_score(CatBoostClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

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994:	learn: 0.0374116	total: 6s	remaining: 30.2ms
995:	learn: 0.0373838	total: 6.01s	remaining: 24.1ms
996:	learn: 0.0373320	total: 6.01s	remaining: 18.1ms
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999:	learn: 0.0371526	total: 6.03s	remaining: 0us
[0.94014085 0.92253521 0.93309859 0.93639576 0.95759717 0.95759717
 0.92932862 0.90812721 0.95053004 0.9540636 ]
0.9389414223859054


from lightgbm import LGBMClassifier
clf=cross_val_score(LGBMClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.9471831  0.92605634 0.91549296 0.95759717 0.96113074 0.9540636
 0.9434629  0.90459364 0.96819788 0.9540636 ]
0.943184193500224


from sklearn.ensemble import AdaBoostClassifier
clf=cross_val_score(AdaBoostClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.76056338 0.68309859 0.73943662 0.6819788  0.7844523  0.75971731
 0.72438163 0.73144876 0.77738516 0.75265018]
0.7395112725824914


from sklearn.ensemble import GradientBoostingClassifier
clf=cross_val_score(GradientBoostingClassifier(),X_train,y_train, scoring='accuracy', cv=skfold)
print(clf)
print(clf.mean())

[0.88380282 0.88380282 0.87323944 0.88339223 0.91872792 0.89399293
 0.90812721 0.87279152 0.92579505 0.91519435]
0.8958866271835962


from sklearn.model_selection import RandomizedSearchCV


RandomForestClassifier
n_estimators = range(200,2100,100)
max_features = ['auto', 'sqrt', 'log2']
max_depth = range(1,100,)
min_samples_split=range(2,11,1)
min_samples_leaf=range(2,11,1)
criterion= ['gini', 'entropy']
bootstrap=['True', 'False']
grid= {'n_estimators': n_estimators, 'max_features': max_features, 'max_depth': max_depth, \
       'min_samples_split':min_samples_split, 'min_samples_leaf': min_samples_leaf, 'criterion': criterion}


rf=RandomizedSearchCV(estimator=RandomForestClassifier(random_state=354), param_distributions=grid, n_iter=10, n_jobs=-1, \
                      cv=skfold, scoring='accuracy', random_state=354, verbose=0)


rf.fit(X_train, y_train)
Best_Score=rf.best_score_
print('best_score: %f' % Best_Score)
print('rf.best_estimator: ', rf.best_estimator_)
RF=rf.best_estimator_

best_score: 0.919528
rf.best_estimator:  RandomForestClassifier(max_depth=38, max_features='sqrt', min_samples_leaf=2,
                       min_samples_split=7, n_estimators=800, random_state=354)


clf=RF
clf.predict(X_train)

#Testing the model using X_test and storing the output in y_pred
y_pred = clf.predict(X_test)
y_pred_train=clf.predict(X_train)
# Creating  a confusion matrix,which compares the y_test and y_pred
cm = confusion_matrix(y_test, y_pred)

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm_df = pd.DataFrame(cm,
                     index = ['Low','Medium','High'], 
                     columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(5,5))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

print('******Training Performance*****')
print(classification_report(y_train, y_pred_train))
print('******Testing Performance*****')
print(classification_report(y_test, y_pred))

******Training Performance*****
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       944
           1       0.99      0.99      0.99       945
           2       0.99      0.99      0.99       944

    accuracy                           1.00      2833
   macro avg       1.00      1.00      1.00      2833
weighted avg       1.00      1.00      1.00      2833

******Testing Performance*****
              precision    recall  f1-score   support

           0       0.96      0.95      0.95       167
           1       0.92      0.87      0.89       166
           2       0.92      0.97      0.94       167

    accuracy                           0.93       500
   macro avg       0.93      0.93      0.93       500
weighted avg       0.93      0.93      0.93       500


n_estimators = range(100,1000,50)
learning_rate= [0.05, 0.1, 0.15, 0.2, 0.25, 0.3,0.35,0.4]
max_features = ['sqrt', 'log2']
max_depth = range(1,50,2)
min_child_weight=[1,3,5,7,9]
gamma= [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8]
colsample_bytree= [0.3,0.4,0.5,0.7,0.8,0.9,1]
max_delta_step= [0.0,0.1,0.2,0.3,0.4,0.5]
reg_alpha=[0.0,0.1,0.2,0.3,0.4,0.5]
reg_lambda=[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0,0.8,0.9]
booster= ['gbtree', 'gblinear', 'dart']

grid={
    'booster': booster,
    'n_estimators': n_estimators,
    'learning_rate': learning_rate,
    'max_depth': max_depth,
    'min_child_weight': min_child_weight,
    'gamma': gamma,
    'colsample_bytree': colsample_bytree,
    'max_delta_step': max_delta_step,
    'reg_alpha': reg_alpha,
    'reg_lambda': reg_lambda,
}

xgb=RandomizedSearchCV(estimator=XGBClassifier(random_state=354,), param_distributions=grid, n_iter=10, n_jobs=-1, \
                      cv=skfold, scoring='accuracy', random_state=354, verbose=0)
xgb.fit(x, y)
Best_Score=xgb.best_score_
print('best_score: %f' % Best_Score)
print('xgb.best_estimator: ', xgb.best_estimator_)
XGB=xgb.best_estimator_

[23:36:06] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.
best_score: 0.950195
xgb.best_estimator:  XGBClassifier(base_score=0.5, booster='dart', colsample_bylevel=1,
              colsample_bynode=1, colsample_bytree=0.9, gamma=0.2, gpu_id=-1,
              importance_type='gain', interaction_constraints='',
              learning_rate=0.2, max_delta_step=0.2, max_depth=17,
              min_child_weight=3, missing=nan, monotone_constraints='()',
              n_estimators=850, n_jobs=12, num_parallel_tree=1,
              objective='multi:softprob', random_state=354, reg_alpha=0.0,
              reg_lambda=0.6, scale_pos_weight=None, subsample=1,
              tree_method='exact', validate_parameters=1, verbosity=None)


clf=XGB
clf.predict(X_train)

#Testing the model using X_test and storing the output in y_pred
y_pred = clf.predict(X_test)
y_pred_train=clf.predict(X_train)
# Creating  a confusion matrix,which compares the y_test and y_pred
cm = confusion_matrix(y_test, y_pred)

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm_df = pd.DataFrame(cm,
                     index = ['Low','Medium','High'], 
                     columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(5,5))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

print('******Training Performance*****')
print(classification_report(y_train, y_pred_train))
print('******Testing Performance*****')
print(classification_report(y_test, y_pred))

******Training Performance*****
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       944
           1       1.00      1.00      1.00       945
           2       1.00      1.00      1.00       944

    accuracy                           1.00      2833
   macro avg       1.00      1.00      1.00      2833
weighted avg       1.00      1.00      1.00      2833

******Testing Performance*****
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       167
           1       1.00      1.00      1.00       166
           2       1.00      1.00      1.00       167

    accuracy                           1.00       500
   macro avg       1.00      1.00      1.00       500
weighted avg       1.00      1.00      1.00       500


boosting_type =['gbdt', 'dart', 'goss', 'rf']
num_leaves =range(10,32,3)
n_estimators = range(200,1000,100)
learning_rate= [0.05, 0.1, 0.15, 0.2, 0.25, 0.3,0.35,0.4]
max_features = ['sqrt', 'log2']
max_depth = range(1,50,5)
min_child_samples=[20,22,25,27,29,30,31]

colsample_bytree= [0.3,0.4,0.5,0.7,0.8,0.9,1]
max_delta_step= [0.0,0.1,0.2,0.3,0.4,0.5]
reg_alpha=[0.0,0.1,0.2,0.3,0.4]
reg_lambda=[0.7,0.75,0.8,0.85,0.9,0.95,1]
importance_type=['split','gain']

grid={
    'boosting_type': boosting_type,
    'n_estimators': n_estimators,
    'learning_rate': learning_rate,
    'min_child_samples': min_child_samples,
    'colsample_bytree': colsample_bytree,
    'importance_type': importance_type,
    'reg_alpha': reg_alpha,
    'reg_lambda': reg_lambda,
    'num_leaves': num_leaves,
    'max_delta_step': max_delta_step
}

lgbm=RandomizedSearchCV(estimator=LGBMClassifier(), param_distributions=grid, cv=skfold, n_iter=10, n_jobs=-1, \
                      scoring='accuracy', random_state=354, verbose=0)
lgbm.fit(X_train,y_train)
Best_Score=lgbm.best_score_
print('Best_Score: %f' % Best_Score)
print('Best_Model: ', lgbm.best_estimator_ )
LGBM=lgbm.best_estimator_

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
10 fits failed out of a total of 100.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
10 fits failed with the following error:
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\sklearn.py", line 967, in fit
    super().fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=valid_sets,
  File "C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\sklearn.py", line 748, in fit
    self._Booster = train(
  File "C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\engine.py", line 271, in train
    booster = Booster(params=params, train_set=train_set)
  File "C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\basic.py", line 2610, in __init__
    _safe_call(_LIB.LGBM_BoosterCreate(
  File "C:\ProgramData\Anaconda3\lib\site-packages\lightgbm\basic.py", line 125, in _safe_call
    raise LightGBMError(_LIB.LGBM_GetLastError().decode('utf-8'))
lightgbm.basic.LightGBMError: Check failed: config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f at d:\a\1\s\python-package\compile\src\boosting\rf.hpp, line 35 .


  warnings.warn(some_fits_failed_message, FitFailedWarning)

Best_Score: 0.947413
Best_Model:  LGBMClassifier(colsample_bytree=0.8, importance_type='gain', max_delta_step=0.2,
               min_child_samples=27, n_estimators=600, num_leaves=28,
               reg_lambda=0.75)


clf = LGBM
clf.predict(X_train)

#Testing the model using X_test and storing the output in y_pred
y_pred = clf.predict(X_test)
y_pred_train=clf.predict(X_train)
# Creating  a confusion matrix,which compares the y_test and y_pred
cm = confusion_matrix(y_test, y_pred)

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm_df = pd.DataFrame(cm,
                     index = ['Low','Medium','High'], 
                     columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(5,5))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

print('******Training Performance*****')
print(classification_report(y_train, y_pred_train))
print('******Testing Performance*****')
print(classification_report(y_test, y_pred))

******Training Performance*****
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       944
           1       1.00      1.00      1.00       945
           2       1.00      1.00      1.00       944

    accuracy                           1.00      2833
   macro avg       1.00      1.00      1.00      2833
weighted avg       1.00      1.00      1.00      2833

******Testing Performance*****
              precision    recall  f1-score   support

           0       0.97      0.97      0.97       167
           1       0.94      0.93      0.93       166
           2       0.96      0.97      0.96       167

    accuracy                           0.96       500
   macro avg       0.96      0.96      0.96       500
weighted avg       0.96      0.96      0.96       500


grid = {'max_depth': [3,4,5],'n_estimators':[100, 200, 300]}

cat=RandomizedSearchCV(estimator=CatBoostClassifier(), param_distributions=grid, cv=skfold, n_iter=10, n_jobs=-1, \
                      scoring='accuracy', random_state=354, verbose=2)
cat.fit(x,y)
Best_Score=cat.best_score_
print('Best_Score: %f' % Best_Score)
print('Best_Model: ', cat.best_estimator_ )
Catboost= cat.best_estimator_

Fitting 10 folds for each of 9 candidates, totalling 90 fits

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
1 fits failed out of a total of 90.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
1 fits failed with the following error:
Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\ProgramData\Anaconda3\lib\site-packages\catboost\core.py", line 4716, in fit
    self._fit(X, y, cat_features, text_features, embedding_features, None, sample_weight, None, None, None, None, baseline, use_best_model,
  File "C:\ProgramData\Anaconda3\lib\site-packages\catboost\core.py", line 2037, in _fit
    self._train(
  File "C:\ProgramData\Anaconda3\lib\site-packages\catboost\core.py", line 1464, in _train
    self._object._train(train_pool, test_pool, params, allow_clear_pool, init_model._object if init_model else None)
  File "_catboost.pyx", line 4393, in _catboost._CatBoost._train
  File "_catboost.pyx", line 4442, in _catboost._CatBoost._train
_catboost.CatBoostError: (The requested operation cannot be performed on a file with a user-mapped section open.) C:/Program Files (x86)/Go Agent/pipelines/BuildMaster/catboost.git/util/system/file.cpp:856: can't open "catboost_info\\catboost_training.json" with mode CreateAlways (0x00000004)

  warnings.warn(some_fits_failed_message, FitFailedWarning)

Learning rate set to 0.232713
0:	learn: 0.9511970	total: 7.97ms	remaining: 2.38s
1:	learn: 0.8549127	total: 12.3ms	remaining: 1.83s
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Best_Score: 0.941494
Best_Model:  <catboost.core.CatBoostClassifier object at 0x000001C275882B20>


clf = Catboost
clf.predict(X_train)

#Testing the model using X_test and storing the output in y_pred
y_pred = clf.predict(X_test)
y_pred_train=clf.predict(X_train)
# Creating  a confusion matrix,which compares the y_test and y_pred
cm = confusion_matrix(y_test, y_pred)

# Creating a dataframe for a array-formatted Confusion matrix,so it will be easy for plotting.
cm_df = pd.DataFrame(cm,
                     index = ['Low','Medium','High'], 
                     columns = ['Low','Medium','High'])

#Plotting the confusion matrix
plt.figure(figsize=(5,5))
sns.heatmap(cm_df, annot=True)
plt.title('Confusion Matrix')
plt.ylabel('Actal Values')
plt.xlabel('Predicted Values')
plt.show()

print('******Training Performance*****')
print(classification_report(y_train, y_pred_train))
print('******Testing Performance*****')
print(classification_report(y_test, y_pred))

******Training Performance*****
              precision    recall  f1-score   support

           0       1.00      1.00      1.00       944
           1       1.00      0.99      1.00       945
           2       1.00      1.00      1.00       944

    accuracy                           1.00      2833
   macro avg       1.00      1.00      1.00      2833
weighted avg       1.00      1.00      1.00      2833

******Testing Performance*****
              precision    recall  f1-score   support

           0       0.99      1.00      1.00       167
           1       1.00      0.99      1.00       166
           2       1.00      1.00      1.00       167

    accuracy                           1.00       500
   macro avg       1.00      1.00      1.00       500
weighted avg       1.00      1.00      1.00       500


def Predictor(Classifier):
    sample=[]
    for i in X_train.columns:
        a= float(input("Enter %s: " %i))
        sample.append(a)
    sample.append(0)
    sample_arr=np.asarray(sample)
    newsam=sample_arr.reshape(1,12)
    sample_scaled=scaler.transform(newsam)
    sample_scaled=np.delete(sample_scaled, -1)
    sample_scaled=sample_scaled.reshape(1,11)

    Prediction = Classifier.predict(sample_scaled)
    print(Prediction)
    if Prediction == '1':
        print("*****Wine quality is Medium*****")
    elif Prediction == '0':
        print('*****Wine quality is Low*****')
    else:
        print('*****Wine quality is High*****')


Predictor(XGB)

Enter fixed acidity: 7.4
Enter volatile acidity: 07
Enter citric acid: 0
Enter residual sugar: 1.9
Enter chlorides: .076
Enter free sulfur dioxide: 11
Enter total sulfur dioxide: 34
Enter density: .99780
Enter pH: 3.51
Enter sulphates: .56
Enter alcohol: 9.4
['1']
*****Wine quality is Medium*****

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	0.247788	0.397260	0.00	0.068493	0.106845	0.140845	0.098940	0.567548	0.606299	0.137725	0.153846	5
1	0.283186	0.520548	0.00	0.116438	0.143573	0.338028	0.215548	0.494126	0.362205	0.209581	0.215385	5
2	0.283186	0.438356	0.04	0.095890	0.133556	0.197183	0.169611	0.508811	0.409449	0.191617	0.215385	5
3	0.584071	0.109589	0.56	0.068493	0.105175	0.225352	0.190813	0.582232	0.330709	0.149701	0.215385	6
4	0.247788	0.369863	0.00	0.061644	0.105175	0.169014	0.120141	0.567548	0.606299	0.137725	0.153846	5
...	...	...	...	...	...	...	...	...	...	...	...	...
1354	0.194690	0.342466	0.08	0.068493	0.093489	0.380282	0.113074	0.472834	0.535433	0.293413	0.169231	6
1355	0.141593	0.328767	0.08	0.075342	0.130217	0.436620	0.134276	0.354626	0.559055	0.149701	0.323077	5
1356	0.115044	0.294521	0.10	0.089041	0.083472	0.535211	0.159011	0.370778	0.614173	0.257485	0.430769	6
1357	0.115044	0.359589	0.12	0.075342	0.105175	0.436620	0.134276	0.396476	0.653543	0.227545	0.276923	5
1358	0.123894	0.130137	0.47	0.184932	0.091820	0.239437	0.127208	0.397944	0.511811	0.197605	0.400000	6

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol
quality
3	0.256637	0.496575	0.035	0.082192	0.131052	0.070423	0.031802	0.550294	0.511811	0.128743	0.234615
4	0.256637	0.376712	0.090	0.082192	0.113523	0.140845	0.070671	0.472100	0.496063	0.137725	0.246154
5	0.283186	0.315068	0.230	0.089041	0.116861	0.197183	0.151943	0.508811	0.440945	0.149701	0.184615
6	0.292035	0.253425	0.270	0.089041	0.110184	0.183099	0.102473	0.473568	0.456693	0.185629	0.323077
7	0.371681	0.171233	0.400	0.095890	0.100167	0.140845	0.074205	0.418502	0.425197	0.245509	0.492308
8	0.292035	0.178082	0.390	0.075342	0.098497	0.098592	0.063604	0.341410	0.385827	0.245509	0.630769

	index	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
count	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000	1356.000000
mean	678.542773	8.155752	0.522721	0.271320	2.190229	0.078883	15.167773	43.502581	0.996703	3.307330	0.634521	10.405789	5.621681
std	392.489293	1.496265	0.168572	0.194358	0.435998	0.015052	9.134808	27.274317	0.001653	0.139595	0.117560	1.035735	0.822615
min	0.000000	4.600000	0.120000	0.000000	0.900000	0.039000	1.000000	6.000000	0.992350	2.930000	0.330000	8.400000	3.000000
25%	338.750000	7.100000	0.390000	0.090000	1.900000	0.070000	7.000000	22.000000	0.995650	3.210000	0.550000	9.500000	5.000000
50%	678.500000	7.900000	0.520000	0.260000	2.200000	0.078000	13.500000	38.000000	0.996690	3.310000	0.610000	10.200000	6.000000
75%	1018.250000	9.000000	0.636250	0.430000	2.400000	0.087000	21.000000	58.000000	0.997800	3.392500	0.710000	11.100000	6.000000
max	1358.000000	12.300000	1.010000	0.790000	3.650000	0.122000	42.000000	124.000000	1.001000	3.680000	0.990000	13.500000	8.000000

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	-0.505279	1.052035	-1.396499	-0.665910	-0.191645	-0.456420	-0.348536	0.663782	1.452377	-0.634128	-0.971446	5
1	-0.237848	2.120219	-1.396499	0.940193	1.270542	1.076745	0.861840	0.058453	-0.769154	0.387003	-0.585104	5
2	-0.237848	1.408096	-1.190617	0.251863	0.871763	-0.018373	0.385025	0.179519	-0.339180	0.131720	-0.585104	5
3	2.035316	-1.440394	1.485851	-0.665910	-0.258108	0.200650	0.605093	0.784848	-1.055803	-0.463940	-0.585104	6
4	-0.505279	0.814661	-1.396499	-0.895354	-0.258108	-0.237397	-0.128468	0.663782	1.452377	-0.634128	-0.971446	5
...	...	...	...	...	...	...	...	...	...	...	...	...
1351	-0.906425	0.577287	-0.984735	-0.665910	-0.723349	1.405280	-0.201824	-0.117093	0.807416	1.578324	-0.874860	6
1352	-1.307572	0.458600	-0.984735	-0.436467	0.738837	1.843327	0.018244	-1.091674	1.022403	-0.463940	0.090994	5
1353	-1.508145	0.161882	-0.881794	0.022420	-1.122127	2.609909	0.274991	-0.958501	1.524039	1.067758	0.767092	6
1354	-1.508145	0.725646	-0.778853	-0.436467	-0.258108	1.843327	0.018244	-0.746636	1.882350	0.642286	-0.198762	5
1355	-1.441287	-1.262363	1.022616	3.234626	-0.789812	0.310162	-0.055112	-0.734529	0.592429	0.216815	0.573921	6

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	-0.505279	1.052035	-1.396499	-0.665910	-0.191645	-0.456420	-0.348536	0.663782	1.452377	-0.634128	-0.971446	1
1	-0.237848	2.120219	-1.396499	0.940193	1.270542	1.076745	0.861840	0.058453	-0.769154	0.387003	-0.585104	1
2	-0.237848	1.408096	-1.190617	0.251863	0.871763	-0.018373	0.385025	0.179519	-0.339180	0.131720	-0.585104	1
3	2.035316	-1.440394	1.485851	-0.665910	-0.258108	0.200650	0.605093	0.784848	-1.055803	-0.463940	-0.585104	1
4	-0.505279	0.814661	-1.396499	-0.895354	-0.258108	-0.237397	-0.128468	0.663782	1.452377	-0.634128	-0.971446	1
...	...	...	...	...	...	...	...	...	...	...	...	...
1351	-0.906425	0.577287	-0.984735	-0.665910	-0.723349	1.405280	-0.201824	-0.117093	0.807416	1.578324	-0.874860	1
1352	-1.307572	0.458600	-0.984735	-0.436467	0.738837	1.843327	0.018244	-1.091674	1.022403	-0.463940	0.090994	1
1353	-1.508145	0.161882	-0.881794	0.022420	-1.122127	2.609909	0.274991	-0.958501	1.524039	1.067758	0.767092	1
1354	-1.508145	0.725646	-0.778853	-0.436467	-0.258108	1.843327	0.018244	-0.746636	1.882350	0.642286	-0.198762	1
1355	-1.441287	-1.262363	1.022616	3.234626	-0.789812	0.310162	-0.055112	-0.734529	0.592429	0.216815	0.573921	1

Clean Duplicate Values¶

Check Outliers¶

Outlier Treatment¶

Standardise the data¶

View Effect of each variable on quality¶

Label Encoding the Target variable¶

Treatment of imbalance dataset¶

Splitting the dataset to Train and test¶

Decision Tree¶

SVM¶

Random Forest¶

KNN¶

Naive-Bayes Classifier¶

XGBoost Classifier¶

CATBoost Classifier¶

LightGBM Classifier¶

Adaboost¶

Gradient Boosting¶

Hyperparameter tuning¶

Random forest¶

XGBoost¶

LightGBM¶

CATBoost¶

Real Time Prediction¶

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.700	0.00	1.9	0.076	11.0	34.0	0.99780	3.51	0.56	9.4	5
1	7.8	0.880	0.00	2.6	0.098	25.0	67.0	0.99680	3.20	0.68	9.8	5
2	7.8	0.760	0.04	2.3	0.092	15.0	54.0	0.99700	3.26	0.65	9.8	5
3	11.2	0.280	0.56	1.9	0.075	17.0	60.0	0.99800	3.16	0.58	9.8	6
4	7.4	0.660	0.00	1.8	0.075	13.0	40.0	0.99780	3.51	0.56	9.4	5
...	...	...	...	...	...	...	...	...	...	...	...	...
1354	6.8	0.620	0.08	1.9	0.068	28.0	38.0	0.99651	3.42	0.82	9.5	6
1355	6.2	0.600	0.08	2.0	0.090	32.0	44.0	0.99490	3.45	0.58	10.5	5
1356	5.9	0.550	0.10	2.2	0.062	39.0	51.0	0.99512	3.52	0.76	11.2	6
1357	5.9	0.645	0.12	2.0	0.075	32.0	44.0	0.99547	3.57	0.71	10.2	5
1358	6.0	0.310	0.47	3.6	0.067	18.0	42.0	0.99549	3.39	0.66	11.0	6